EODIE — Earth Observation Data Information Extractor
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023-07
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Language
en
Pages
9
Series
SoftwareX, Volume 23
Abstract
Remote sensing satellites provide a vast amount of data to monitor and observe Earth's surface and events on it. To use these data efficiently in subsequent analysis and decision-making, highly automated easy-to-use tools are needed. Here, we present Earth Observation Data Information Extractor (EODIE). EODIE is a toolkit to extract object-level time-series information from several multispectral satellite remote sensing platforms and to produce analysis-ready products for subsequent data analysis. EODIE has a modular design that makes it adjustable for end-user requirements. Users have a possibility to exchange and add modules in EODIE for flexible processing in different computing environments. With EODIE, remote sensing data can be processed to object level array, geotiff or statistics information of different (vegetation) indices or plain wavelength intervals.Description
Funding Information: This project was initiated under the Academy of Finland research project 295047 in collaboration with Paula Litkey and Miloš Pandžić and has been supported also from the project 316096/320075. Part of the work has also been done under the umbrella of Academy of Finland flagship project UNITE (337656). Ms. Wittke acknowledges the PhD grant from Aalto School of Engineering . The authors wish to thank CSC – IT Center for Science and the Open Geospatial Information Infrastructure for Research (oGIIR, urn:nbn:fi:research-infras-2016072513) for generous computational resources and guidance. Maria Yli-Heikkilä from Natural Resources Finland (LUKE) is acknowledged for providing Sentinel-2 data on CSC object storage for the example use case as a part of the Eurostat project ”Development of pre-harvest crop yield forecasts with satellite remote sensing data” (EC grant agreement No 831735). We are also grateful for the constructive comments on the documentation by the Nordic-RSE community (Richard Darst, Radovan Bast, Luca Ferranti, Enrico Glerean and Matthew West) and valuable comments on the manuscript by Katri Tegel. The development of the EODIE Galaxy Tool has been supported by EOSC-Nordic, a project funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857652 . Funding Information: This project was initiated under the Academy of Finland research project295047 in collaboration with Paula Litkey and Miloš Pandžić and has been supported also from the project 316096/320075. Part of the work has also been done under the umbrella of Academy of Finland flagship project UNITE (337656). Ms. Wittke acknowledges the PhD grant from Aalto School of Engineering. The authors wish to thank CSC – IT Center for Science and the Open Geospatial Information Infrastructure for Research (oGIIR, urn:nbn:fi:research-infras-2016072513) for generous computational resources and guidance. Maria Yli-Heikkilä from Natural Resources Finland (LUKE) is acknowledged for providing Sentinel-2 data on CSC object storage for the example use case as a part of the Eurostat project ”Development of pre-harvest crop yield forecasts with satellite remote sensing data” (EC grant agreement No 831735). We are also grateful for the constructive comments on the documentation by the Nordic-RSE community (Richard Darst, Radovan Bast, Luca Ferranti, Enrico Glerean and Matthew West) and valuable comments on the manuscript by Katri Tegel. The development of the EODIE Galaxy Tool has been supported by EOSC-Nordic, a project funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 857652. Publisher Copyright: © 2023 The Author(s)
Keywords
Big data processing, Earth observation, Open-source software, Remote sensing
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Citation
Wittke, S, Fouilloux, A, Lehti, P, Varho, J, Kivimäki, A, Karhu, M, Karjalainen, M, Vaaja, M & Puttonen, E 2023, ' EODIE — Earth Observation Data Information Extractor ', SoftwareX, vol. 23, 101421 . https://doi.org/10.1016/j.softx.2023.101421